Cost Oracle & FinOps Agent Modeling

Implements pre-flight predictive token cost estimation, model pricing tier arbitrage, and autonomous budget forecasting for multi-agent workflows across Google Gemini 2.0 Flash, Gemini 2.0 Pro, and Gemini 3.8 models.

1. Architecture: Pre-Flight Financial Oracle

┌─────────────────────────────────────────────────────────────────────────────┐
│                        PRE-FLIGHT COST ORACLE ENGINE                        │
│                                                                             │
│   ┌─────────────────────┐                                                   │
│   │ Proposed Task &     │                                                   │
│   │ Subagent Trajectory │                                                   │
│   └──────────┬──────────┘                                                   │
│              │                                                              │
│              ▼                                                              │
│   ┌─────────────────────────────────────────────────────────────────────┐   │
│   │ 1. Token Predictor (Context Caching vs. Dynamic Suffix Heuristics)  │   │
│   │    • System & Skill Frontmatter Tokens                              │   │
│   │    • Expected Reasoning Depth (Extended Thinking Tokens)            │   │
│   │    • Tool Call Frequency & Speculative Probing Branches             │   │
│   └──────────────────────────────────┬──────────────────────────────────┘   │
│                                      │                                      │
│                                      ▼                                      │
│   ┌─────────────────────────────────────────────────────────────────────┐   │
│   │ 2. FinOps Pricing Engine (Per Model Rates & Discount Tiers)        │   │
│   │    • Gemini 2.0 Flash: $0.075 / 1M input, $0.30 / 1M output         │   │
│   │    • Context Caching: 75% input discount ($0.01875 / 1M input)      │   │
│   │    • Gemini 2.0 Pro: $1.25 / 1M input, $5.00 / 1M output            │   │
│   └──────────────────────────────────┬──────────────────────────────────┘   │
│                                      │                                      │
│                                      ▼                                      │
│   ┌─────────────────────────────────────────────────────────────────────┐   │
│   │ 3. Pre-Flight Financial Gating (Pass / Downgrade / Deny)            │   │
│   │    • If Expected Cost > MAX_USD_BUDGET -> Abort or switch to Flash  │   │
│   │    • If Expected Cost <= Budget Ceiling -> Issue Financial Clearance │ │
│   └─────────────────────────────────────────────────────────────────────┘   │
└─────────────────────────────────────────────────────────────────────────────┘

2. Mathematical Cost Modeling Formula

Let $T_{in}$ be input tokens, $T_{cache}$ be context-cached prefix tokens, $T_{out}$ be output generation tokens, and $T_{think}$ be internal reasoning tokens:

$$\text{Cost} = (T_{cache} \times P_{cache}) + ((T_{in} - T_{cache}) \times P_{in}) + ((T_{out} + T_{think}) \times P_{out})$$

Where for Gemini 2.0 Flash:

• $P_{in} = $0.075 / 10^6$
• $P_{cache} = $0.01875 / 10^6$
• $P_{out} = $0.30 / 10^6$

3. Python Pre-Flight Cost Estimator

from dataclasses import dataclass
from typing import Dict, Any

@dataclass
class CostEstimate:
    model: str
    expected_input_tokens: int
    cached_prefix_tokens: int
    expected_output_tokens: int
    expected_thinking_tokens: int
    estimated_usd: float
    confidence_interval: tuple[float, float]

class FinOpsCostOracle:
    RATES = {
        "gemini-2.0-flash": {
            "input": 0.075 / 1_000_000,
            "cache": 0.01875 / 1_000_000,
            "output": 0.30 / 1_000_000
        },
        "gemini-2.0-pro": {
            "input": 1.25 / 1_000_000,
            "cache": 0.3125 / 1_000_000,
            "output": 5.00 / 1_000_000
        }
    }

    def estimate_task_cost(
        self,
        model: str,
        prompt_length: int,
        active_skills_count: int,
        estimated_turns: int = 5,
        uses_extended_thinking: bool = False
    ) -> CostEstimate:
        rates = self.RATES.get(model, self.RATES["gemini-2.0-flash"])
        
        # Heuristics based on empirical benchmarks
        cached_prefix = 32_768 if active_skills_count > 10 else 0
        input_per_turn = prompt_length + (active_skills_count * 250)
        output_per_turn = 800
        thinking_per_turn = 2048 if uses_extended_thinking else 0
        
        total_input = input_per_turn * estimated_turns
        total_output = output_per_turn * estimated_turns
        total_thinking = thinking_per_turn * estimated_turns
        
        cost = (
            (cached_prefix * rates["cache"]) +
            (max(0, total_input - cached_prefix) * rates["input"]) +
            ((total_output + total_thinking) * rates["output"])
        )
        
        # 95% Confidence Interval (+/- 25% due to variable branching)
        ci = (round(cost * 0.75, 4), round(cost * 1.35, 4))
        
        return CostEstimate(
            model=model,
            expected_input_tokens=total_input,
            cached_prefix_tokens=cached_prefix,
            expected_output_tokens=total_output,
            expected_thinking_tokens=total_thinking,
            estimated_usd=round(cost, 4),
            confidence_interval=ci
        )

4. Enterprise FinOps Best Practices